Methods and systems for automating carbon footprinting

ABSTRACT

Methods and systems for automating carbon footprinting are disclosed. In some embodiments, the methods include a plurality of steps. In some embodiments, related to predetermined resources associated with an item from predetermined data sources is obtained. Then, estimated emission factors are calculated for each of the resources. Next, a contributory uncertainty of the data and of the emission factors is determined. Then, a user is guided based on a comparison of the respective contributory uncertainty of data related to the resources or emission factors. Next, both data related to the resources and the estimated emission factors of the resources are utilized to determine a carbon footprint of the item.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.13/578,297, filed Aug. 10, 2012, issued Dec. 20, 2016 as U.S. Pat. No.9,524,463, which was a National Stage Entry of International PatentApplication PCT/US2011/025116, filed Feb. 16, 2011, which claimed thebenefit of U.S. Provisional Application Nos. 61/304,800, filed Feb. 16,2010, 61/367,165, filed Jul. 23, 2010, and 61/387,218, filed Sep. 28,2010, each of which is incorporated by reference as if disclosed hereinin its entirety.

BACKGROUND

Life cycle analysis (LCA) has been practiced since the 1960s. When in1969, a well-known beverage company commissioned a comparative study oftraditional, re-usable glass bottles vs. plastic bottles, this arguablymarked the debut of LCA as integral to product development even for massmarket consumer goods and services companies. More recently, the morewidespread public awareness of the risks of global warming and the roleof anthropogenic greenhouse gas (GHG) emissions has prompted arenaissance of LCA concepts in the form of standardized carbonfootprinting (CF) of products and services.

Companies usually seek to quantify CFs for one or more of the followingreasons: (i) internal transparency and identification of carbonreduction strategies; (ii) communication of results to externalstakeholders such as environmental monitoring groups, or to apply forcertification; or (iii) requests from a company's corporate customersfor scope 3-relevant data, to use in their corporate carbon accounting.

While LCA has continuously evolved, prompting both often re-citedcriticism and improvement, the new need for accurate and comparable CFshas catalyzed efforts to overcome many of LCA's traditional shortcomingsand provided standards for CF. Today, companies embarking on CF benefitfrom detailed protocols, industry/sector specific guidance, softwarepackages, and databases that provide support with the following: (i)choice of functional unit; (ii) system boundaries; (iii) emissionfactors (EFs) of materials and activities; and (iv) specialty issuessuch as recycling and biogenic carbon and storage. Crucially, guidelinesalso provide a more head-on approach to materiality and realisticallyachievable levels of accuracy. For example, the rounding rules of the UKCarbon Trust imply that even a best-practice CF will have a residualuncertainty of 5-10%.

While the above developments represent tremendous progress andimprovements over the status quo even just a few years ago, quantifyingthe CFs for hundreds or thousands of individual products/services iscurrently impossible, short of a massive buildup of a company'sdedicated personnel and LCA expertise. Specifically, practitioners todayface two fundamental obstacles when performing CF at the scale of largecompanies:

1) required time and expertise: collecting, organizing, and validatingLCA inventory (easily hundreds of data items for a singleproduct/service), as well as mapping to EFs, typically takes hundreds ofman hours and specialized knowledge; and

2) lack of uniformity and integrated platform: CF today is usuallyperformed as a series of one-off efforts, e.g., using non-interlinked,separate spreadsheets for the CF of each new product/service; once thepractitioner has completed data entry and calculation for one product,to the desired accuracy, the practitioner moves on to the next product,often without maximizing the re-use of any previously collectedinformation.

The obstacles related to known LCA practices result in missedopportunities that currently prevent CF from realizing its full spectrumof possible benefits, which include the following:

(1) What-if impacts across products, carbon management, and cost-benefitevaluations: Arguably, one of the greatest opportunities of CF is toenable a company to identify and prioritize reduction strategies.However, because the CFs for a set of different products are usuallycalculated in a set of non-integrated files, it is difficult to quantifythe combined impact of a reduction strategy. For example, counting allimpacts on raw materials, transportation, and disposal, what would bethe total company-wide GHG reductions if all PET packaging were made 15%lighter? What if all factories in a country moved 30% of their primaryenergy consumption to hydropower-rich electricity? Which LCA stages inthe supply chain—measured across all products or by business line—offerthe largest reduction potential? Given an assumed carbon price, wouldthe costs for required upgrades (e.g., modified energy mix, packaging,or ingredients) be a worthwhile investment?

(2) Flexibility vis-à-vis regulatory change: Standards for CF are stillevolving. With current practice, a future change in the CF “accountingrules” would mean tremendous time and resource effort on behalf of acompany, to essentially fix the manual CF calculations for hundreds ofproducts/services. This poses significant “regulatory” risk.

(3) Synergy with corporate carbon accounting (“corporate footprint”):There is a direct relationship between the various LCA stages that counttoward a product/service CF and those that count towards a corporatefootprint. Therefore, there are significant synergies between the datacollection and analyses for product/service CFs and the scopes 1, 2, and3 of corporate footprints. Current CF practice often lacks the coverage,uniformity, or transparency that would enable the company to make use ofsuch synergies

SUMMARY

Methods and systems according to the disclosed subject matter embody oneor more of the following three techniques. First, each CF is based on asingle, uniform data framework that applies to all products/services.Rather than manually, data is entered, wherever possible, via auto-feedsfrom existing enterprise data, e.g., BOM (bill of materials) and energyusage at company-controlled factories. This technique minimizes thenumber of data items that require manual input. Second, particularly forremaining data entries, concurrent uncertainty analysis points the userto those activity data or EFs where additional data or improved accuracywould most improve the accuracy of the calculated CFs. This techniqueuses manual entries more efficiently. Third, a statistical modelapproximates EFs, thereby eliminating the manual mapping of aproduct/service's inventory to the vast selection of EF databases.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings show embodiments of the present disclosure for the purposeof illustrating the invention. However, it should be understood that thepresent application is not limited to the precise arrangements andinstrumentalities shown in the drawings, wherein:

FIG. 1 is a schematic diagram of a system according to some embodimentsof the disclosed subject matter.

FIG. 2 is a chart of a method according to some embodiments of thedisclosed subject matter; and

FIG. 3 is a chart of computer-executable instructions according to someembodiments of the disclosed subject matter.

DESCRIPTION

Referring now to FIGS. 1 and 2, some embodiments include systems andmethods for automating carbon footprinting. Some embodiments include asystem 100 for determining the carbon footprint of an item, such as aproduct or service. Some embodiments of system 100 include the followingcooperating modules that transform data to determine the carbonfootprint of an item: a data automation module 102; an emission factorestimator module 104; a calculation module 106; and a user guidancemodule 108.

Referring now to FIG. 1, data automation module 102 includes mechanismsfor obtaining data 110 related to predetermined resources 112 associatedwith an item 114 from predetermined data sources 116. Resources 112typically include materials and activities expended during themanufacture or production of item 114. Predetermined data sources 116typically, but not always, include an enterprise resource planning (ERP)program database 118. Mechanisms for obtaining data 110 include anautomated data feed 120 mapped to enterprise resource planning programdatabase 118. Other mechanisms include manual data entry or automateddata feeds to myriad databases. In some embodiments, data obtained byautomated data feed 120 include data required by emission factorestimator module 104 for calculating the emission factors of resources112.

Referring now to Table 1, certain data items required for product CFsare generally readily available from existing ERP systems or similardata warehouses.

TABLE 1 Suggested taxonomy of LCA stages and possible data sources. LCAStage Data source Purchased ERP system or similar data warehouses(specifically goods - packaging “bill of materials”). Purchased ERPsystem or similar data warehouses (specifically goods - other “bill ofmaterials”). Transportation - Usually, manual entries orcountry-specific default inbound settings (unless available in ERP).Also note that many EFs of purchased goods may be inclusive of theinbound transportation (“to gate”). Production ERP system or similardata warehouses, such as factory energy consumption monitoring.Transportation - Usually, manual entries or country-specific defaultoutbound settings (unless available in ERP). Distribution Usually,manual entries or country-specific default and retail settings (some ofwhich could be based on sector- specific guidance). Use phase Same asfor “distribution and retail”. Disposal Usually, manual entries orcountry-specific default (end of life) settings. Note these are usuallyat the level of material classes (such as “glass”, “cardboard”,“plastic”, etc.).

In some embodiments of the disclosed subject matter, rather thanmanually entering data manually, conduits control automatic uploads ofas many data inputs as possible. This also allows, for example,quarterly updates, which offers the additional advantage of keepingfootprint results more up to date than with traditional, labor-intensivemodels. Where certain purchased goods cannot be mapped across productsand to EFs via unique IDs, in some embodiments, fuzzy logic-type mappingis used to maximize automatic entry, e.g., “farm potatoes” used forfrozen fries should be mapped to “farm potato” used for crisps, and bothshould be mapped to the country-relevant EF for “potatoes”.

Emission factor estimator module 104 including mechanisms, e.g.,software 122, for calculating estimated emission factors for each ofresources 112. As described below with respect to FIG. 3, software 122includes a statistical model that approximates EFs, thereby eliminatingthe manual mapping of a product/service's inventory to the vastselection of EF databases.

Some embodiments of the disclosed subject matter allow for overrides ofautomatically obtained data and automatically estimated data/factors ona case by case basis. For example, customized EFs that are based onprimary, product-specific data can be manually entered instead ofautomatically estimated.

Calculation module 106 includes mechanisms, e.g., software 124, forutilizing both data related to resources 112 and the estimated emissionfactors of the resources to determine a carbon footprint 126 of item114.

Software 124 includes an algorithm for calculating carbon footprint 126that interprets table of data related to an item, uses look-ups from allother tables of data, and calculates the CF according to a chosenprotocol. In almost all cases, the CF can be represented by a summationover many multiplications, where each multiplication represents the CFcontribution from a particular item (equation (1)). Equation (1) islinear in Di (and in the EFs).

Let CF denote the footprint of an item including a product, set ofproducts, or a carbon reduction strategy, driven by a set of input dataD_(i), e.g., transportation distance, electricity consumption, EF, eachof which varies by a certain coefficient of variation (CV_(i)) aroundits mean. We approximate the resulting uncertainty of CF via a sum ofits partial derivates:

$\begin{matrix}{{{CF}\text{∼}}\;:={{\overset{\_}{CF} + {\sum\limits_{i}{\Delta \; {CF}_{i}}}} = {\overset{\_}{CF} + {\sum\limits_{i}{( {D_{i} - \overset{\_}{D_{i}}} ) \cdot \frac{\partial{CF}}{\partial D_{i}}}}}}} & (1)\end{matrix}$

Where CF denotes the footprint with all D_(i) set to a specific valuefrom their respective distribution, and

denotes the approximation of this CF;

CF denotes the arithmetic mean of CF (if CF is linear in all D_(i), thenCF equals CF when all D, are set to their arithmetic means D _(i)); and

∂CF/∂D_(i) denotes the partial derivative of CF by D_(i), evaluated atD_(i) set to D _(i).

In some embodiments, system 100 includes user guidance module 108including mechanisms, e.g., software 130, for determining a contributoryuncertainty of the data and contributory uncertainty of the emissionfactors and for prompting a user 132 to enter data related to resources112 or emission factors of the resources based on a comparison 134 ofthe respective contributory uncertainty of data related to the resourcesand emission factors.

Software 130, which includes an uncertainty algorithm, uses thecompounded uncertainty of the total CF (of a roll-up or carbon reductionscenario), which is calculated using Equation (1). Equation (1)represents a simple sum, where the variance of CF equals zero and thevariance of each ΔCF_(i) equals the square of

${CV}_{i} \cdot \overset{\_}{D_{i}} \cdot {\frac{\partial{CF}}{\partial D_{i}}.}$

Based on standard stochastics, the variance of a sum across(uncorrelated) components equals the sum of the variances of eachcomponent, whether the distribution of the components are Gaussian ornot. Furthermore, since CF is linear in D_(i), we can rewrite thepartial derivatives as finite differences of CF evaluated at differentvalues of each D_(i). Thus we obtain:

$\begin{matrix}{= \frac{\sqrt{\sum\limits_{i}\lbrack {{{CF}( {\overset{\_}{D_{i}} + {\overset{\_}{D_{i}} \cdot {CV}_{Di}}} )} - {\overset{\_}{ {CF} \rbrack}}^{2}} }}{\overset{\_}{CF}}} & (2)\end{matrix}$

Where

CV

denotes the CV of the (approximated) CF

CV_(Di) denotes the CV of D_(i)

CF(D _(i)+D _(i)+CV_(Di)) denotes CF evaluated at D _(j) plus onestandard deviation (and all other D_(j) at D _(j)).

Equation (2) is essentially the sum across the squares of individual“impacts” (followed by square root and division by CF), where each suchimpact is the change in CF if one Di is increased by one standarddeviation (while all others are kept at their mean, i.e., “ceterisparibus”).

In addition to determining the uncertainty of the CF calculation. Insome embodiments, the uncertainty of each data input is determined as itis input, which enables the overall quality of the inputs to beincreased in real time. By way of example, Equation (2) allows one tocalculate that the CF of a bag of potato chips is 110 g CO2e±18%.Assuming that such a CF calculation is driven by 3 uncertain inputs,e.g., the number of kWh consumed during production, the EF of thepackaging material, and the (average) transportation distance fromfactories to stores, to reduce the CV of the CF to something moreaccurate, it is helpful to break the CV down into the contributions fromeach driver, i.e., to learn which one of the three inputs contributesthe most to the CV and which one the least. As follows, time/effort canthen be focused on improving the accuracy of the inputs that have thebiggest impact on the accuracy of the overall footprint, e.g., of aproduct, set of products, or carbon reduction strategy. In someembodiments, to allocate the CV of the total CF based on each inputs(Di's) contribution to the variance, the following Equation (3) is used:

$\begin{matrix}{{CV}_{C,{Di}} = {\; \cdot \frac{\lbrack {{{CF}( {\overset{\_}{D_{i}} + {\overset{\_}{D_{i}} \cdot {CV}_{Di}}} )} - {\overset{\_}{ {CF} \rbrack}}^{2}} }{\sum\limits_{j}\lbrack {{{CF}( {\overset{\_}{D_{j}} + {\overset{\_}{D_{j}} \cdot {CV}_{Dj}}} )} - \overset{\_}{CF}} \rbrack^{2}}}} \\{= \frac{\lbrack {{{CF}( {\overset{\_}{D_{i}} + {\overset{\_}{D_{i}} \cdot {CV}_{Di}}} )} - \overset{\_}{CF}} \rbrack^{2}}{\; \cdot {\overset{\_}{CF}}^{2}}}\end{matrix}$

Where CV_(C,Di) denotes the contribution of input driver D_(i) to thetotal uncertainty CV

; and

All others as above.

Using Equation (3), the contributory CVc, Di sum up to the CV of CF,which is typically less than 100%, e.g.: the CV of the electricityconsumption contributes 1%, the CV of the transportation distance 6%,and the CV of the packaging EF 11%. The CV contribution, as definedabove, is sensitive to both the CV of a driver Di as well as thedriver's absolute impact on CF.

In some embodiments, the data obtained by data automation module 102 issorted into predetermined tables of data thereby defining a uniform datastructure. In addition to the overall data structure described in Table1, in some embodiments, data is organized into distinct look-up tables.These look-up tables reflect redundancy in the data, such that each CFessentially becomes a permutation of various elements in the tables.Note that most tables store two sets of data, one for the mean and onefor the associated uncertainty of the respective datapoint.

In some embodiments, the look-up tables include the following fivedistinct look-up tables (A)-(E):

(A) Products: This table stores inventory for all LCA stages, for allitems, i.e., products/services of a company. It covers material andactivity data such as amounts of purchased goods, including scrap,spillage, etc., production and distribution, transportation routes, anduse-phase characteristics. In addition, table A stores productattributes such as country, brand, business line, and annual productionvolume (to report roll-ups and breakdowns of the CFs by variouscharacteristics).

(B) Assemblies: This table stores information on those materials andactivities that constitute sub-products or sub-services in themselves.For example, an assembly may specify that a kg of oranges specified intable A refers to a set of purchased goods (fertilizers, manure,pesticides) and activities (fertilizing, pruning, harvesting).Practitioners will have to maintain the information for such assembliesonly once, and then all products that use oranges from the same supplierare mapped to the same assembly.

(C) Purchased goods library: This table stores material-level metainformation to further specify each purchased good contained in eithertable A or table B, for example to determine EFs.

(D) EFs: This table stores EFs for any purchased good (ingredients,packaging) and activity occurring in either table A or table B. Notethat some practitioners employ EFs at the level of assemblies. Whilethis facilitates calculating a CF, e.g., instead of adding up multipleitems in the assembly, simply multiply Weight_(Orange)×EF_(Orange), itobscures granular reporting of the resulting CF by purchased good,production, transportation, and thus reduces the usefulness of theresulting analyses with regards to carbon reduction strategies.

(E) Standards & defaults: This table stores global and country-specificdefault values as described above.

In some embodiments of system 100, at least a portion of the dataobtained by data automation module 102 and the emission factors ofresources 112 determined by emission factor estimator module 104 isgenerated via an estimate based on data and emission factors related tosimilar materials and activities. For example, in some embodiments, theestimate includes averaging data and emission factors related to similarmaterials and activities.

Some embodiments of the disclosed subject matter include self-learninglook-up tables. By quantifying contributory uncertainties at all stagesof the CF calculation, the system can automatically select the availabledata entries with the lowest contributory uncertainty without separateuser intervention thereby expediting the calculation. For example, insome embodiments, the parameters for the CF estimates are automaticallyupdated to always reflect any recently added bottom-up CFs. Similarly,instead of the default plant-to-store distance in a given country, insome embodiments, the system utilizes the average respective distancefrom any other products in that country that were already characterizedbottom up, as soon as the associated uncertainty of that sample fallsbelow the one specified for the default distance.

Some embodiments of the disclosed subject matter include the use ofwizards. The uniform data structure facilitates the creation ofassisted-data-entry tools such as wizards to guide users throughquantifying the CF for any product/service. This is especially helpfulfor analyzing or developing new products whose data is not yet availablethrough ERP-systems or similar data warehouses and therefore must beentered manually.

Referring now to FIG. 2, some embodiments of the disclosed subjectmatter include a method 200 for determining the carbon footprint of anitem such as a product or service. At 202, input data related topredetermined resources associated with an item is obtained frompredetermined data sources. Resources associated with an item typicallyinclude materials and activities expended or conducted in themanufacture or generation of the item. Predetermined data sourcesinclude data from an enterprise resource planning program database, atleast a portion of which is provided via an automated data feed that ismapped to the database. As used herein, enterprise resource planningprogram is broadly defined to include any types of programs anddatabases that include similar types of enterprise data. In someembodiments, method 200 includes the use of fuzzy logic to identifyparticular data in the enterprise resource planning program database. At204, estimated emission factors are calculated or obtained. For theestimated emission factors that are calculated, the calculations includedata obtained by the automated data feeds. At 206, the data and/orestimated emission factors obtained are sorted into predetermined tablesof data according to a uniform data structure. At 208, utilizing bothdata related to the resources, the estimated emission factorscalculated, and predetermined emission factors obtained, a carbonfootprint of the item is calculated. At 210, a contributory uncertaintyof each of the input data and a contributory uncertainty of each of theemission factors is determined. At 212, a comparison of the respectivecontributory uncertainty of each of the input data and each of theemission factors is performed. In addition to guiding a user to enterdata, in some embodiments, the comparison in 212 is the decision basisto choose between many different possible candidates/sources for datainputs, such as data input from another user, data from anotherpredetermined source, data from another estimating algorithm, andaverages of other already existing data inputs. For example, in someembodiments, data related to the resources or emission factors of theresources is automatically obtained via an automated data feed orautomatically generated via calculations based on a comparison of therespective contributory uncertainty of data related to the resources oremission factors. Over time method 100 and related systems becomeself-learning and evolving as their databases grow. At 214, based on thecomparison in 212, a user is guided and prompted to enter data relatedto the resources or emission factors of the resources. Then, at 206,data entered is sorted into predetermined tables of data according to auniform data structure. In some embodiments, the data entered is used torecalculate estimated emission factors at 204.

Referring now to FIG. 3, some embodiments of the disclosed subjectmatter include computer-executable instructions 300 for estimating anemission factor for a resource such at a material or activity expendedor undertaken in the manufacture, generation, or acquisition of an item.Instructions 300 are typically in the form of a software programprovided as a computer-readable medium. At 302, in some embodiments,instructions 300 include categorizing the resource in a predeterminedcluster of similar resources. At 304, an average estimated emissionfactor (EF _(Cluster,i)) for the cluster is calculated or obtained. At306, an estimated price (Price) of the resource is obtained. In someembodiments, the EF _(Cluster,i) and Price_(i) are automaticallyobtained from an enterprise resource planning program database. At 308,the estimated emission factor (EF_(i,estimated)) for the resource iscalculated according to the following equation:

EF _(i,estimated) =a+b·ln( EF _(Cluster,i))+c·ln(Price_(i));

EF_(i,estimated) denotes the model-generated EF for the material i (in gCO₂e per g);a, b, and c denote the three coefficients that are calibrated/optimized;EF _(Cluster,i) denotes the average of all known EFs that share the samecluster as material i; andPrice_(i) denotes the price of material i (in USD per kg).

Clustering involves using the average EF of a respective cluster, i.e.,sorted group of like data, as an approximate EF for the material/processin question. In one analysis, when approximating each of 1758 EFs inwith the average EF of the respective cluster, the CV of the thusestimated EFs ranged from 0% to 476% (depending on the cluster). Theaverage of the CVs of all 77 clusters was 91%.

In some embodiments of a software program including instructions 300, auser manually enters a few characteristics of the material or process,and the program generates an estimated EF. In other embodiments, thecharacteristics required as inputs to the model are chosen such thatthey are automatically available through a company's ERP system orsimilar data warehouses, e.g., material type, price, etc. In suchembodiments, manual intervention to determine EFs is longer required.Regardless, a user retains insight into the accuracy of the fullyautomatically generated CFs via a concurrent uncertainty analysis.

In some embodiments, software programs including instructions 300 are“trained” using the thousands of known EFs from LCA studies and publicor commercial EF databases. Significant amounts of meta data such asgeography, boundaries, e.g., to farm, to gate, in/excluding biogenic,etc. are also input to the software program. In some embodiments, neuralnetwork based algorithms are used to estimate EFs.

Methods and systems according to the disclosed subject matter offerbenefits and advantages over known technologies. Methods and systemsaccording to the disclosed subject matter reduce the count of requiredmanual data entries by as much as a factor of 1000 vs. current practice,depending on how much data can be imported from ERP systems or similardata warehouses.

In comparison with current CF practice, which are usually manual,product-by-product calculations in multiple, non-interlinkedspreadsheets, methods and systems according to the disclosed subjectmatter offer at least the following advantages:

(1) Scalability: CF for hundreds or thousands of products is currentlysimply impossible short of a massive buildup of a company's dedicatedpersonnel and LCA expertise. Methods and systems according to thedisclosed subject matter make the process scalable to a company's globalproduct/service portfolio. Linking the CF to automatic ERP/datawarehouse uploads, with, for example, quarterly updates, also allowcompanies to keep footprints current. Any changes in factory energyusage or raw material consumptions, e.g., less spill, are captured inthe next round of footprints, without requiring manual updates.

(2) Transparency: The concurrent uncertainty analysis assists a user inimproving overall speed and accuracy, by identifying those input datathat currently contribute the most to the uncertainty of the CF resultin question. The uniform structure of drivers and algorithms assist thepractitioner in comparing CFs, including the traditionally difficultanalysis of changes in baseline vs. actual CF and detailed product orprocess comparisons of two CFs with overlapping error margins.

(3) Carbon management and cost/benefit evaluations: Knowing CFs for allproducts, including breakdowns by LCA stages, allows “slicing anddicing” the CFs for the company's global portfolio in any desired wayincluding national roll-ups, by product type, by business line andbreak-downs of the company's total product CF, e.g., by packaging,transportation, disposal, etc. Carbon reduction strategies such aslight-weighting the packaging, substituting purchased goods withlow-carbon alternatives, improving distribution and transportationefficiency, etc. can be evaluated instantly, and the resulting changesin GHG emissions can be compared with the estimated investment costs ofthe initiative.

(4) Certification and communication with eco-labeling groups: Input dataand algorithmic details such as allocation rules are transparent andsuch that the resulting CF for individual products are easilycertifiable, based on system-generated, detailed reports.

(5) Synergies with corporate GHG reporting, especially scope 3:Product/service CF and corporate GHG reporting (scopes 1, 2, and 3) areoften performed as separate efforts, by different teams, and withdifferent datasets. Methods and systems according to the disclosedsubject matter can unlock important synergies between the two reportingefforts.

(6) Low regulatory risk: Because specific CF algorithms operate inparallel to all inventory data, on an integrated platform, any changesto the CF “accounting rules”, e.g., treatment of recycling, can easilybe implemented by adjusting respective parts of the software code.

In typical CF and wider LCA studies, uncertainty analysis, if includedat all, is carried out only after all data have been collected and thefootprint quantified. Methods and systems according to the disclosedsubject matter use analytic error propagation carried out concurrentlywith all data entry and CF calculations. This approach provides, at anypoint in time, full transparency into (i) the uncertainty (standarddeviation) of the calculated results and (ii) which input datacontributes how much to the uncertainty of each CF, thus facilitating amore focused and efficient effort to improve data quality in the overallsystem. Concurrent uncertainty analysis points a user to those activitydata or EFs where additional data or improved accuracy would mostimprove the accuracy of the calculated footprints. This technique usesmanual entries more efficiently.

With current CF practice, carbon management analyses only becomepossible once all relevant product/service have been footprinted one byone. Methods and systems according to the disclosed subject matteraccelerates this, by focusing on entering data that the system can usefor many products simultaneously, e.g., number of days of refrigerationof all beverage products in a certain country, even if the data may notyet have the desired accuracy. Hence, additional time spent on dataentry, e.g., for those data items that are not automatically loaded fromERP systems, is used to increase the accuracy of all CFs and reportinganalyses, rather than the cumulative number of individual products thathave been footprinted. Concurrent uncertainty analyses quantify theremaining uncertainty, for an individual CF, for a roll-up of CFs, orfor specific reports. For example, a practitioner may find that thereport “what is the relative contribution of PET to our overall CFacross all products” can already be performed, to sufficient accuracy,before specific refrigeration times have been entered for every productindividually (because the contribution of the refrigeration activity tothe uncertainty of the overall CF may be small).

Methods and systems according to the disclosed subject matter enablemulti-user input. For example, one user may improve the accuracy of acertain EF (by updating from the system-provided to a morebespoke/primary value) while, at the same time, another practitionersimultaneously updates the aluminum recycling rate in China. The systemthen simultaneously updates the CFs of all products that require one orboth of these inputs, thus minimizing overall required resources.

A single, uniform data structure is used for all products/services. Auniform data structure is particularly advantageous for at least threereasons. First, comparability is different from accuracy. Suppose wereview the CFs of two “competing” products, one 10% smaller than theother, but both with a margin of error (standard deviation) of ±20%(often referred to as “overlapping error margins”). While we may notknow the true, absolute CF of either (only to within +20%), there willstill be many such situations where we can say with certainty that oneCF is definitely smaller than the other (and thus the product orassociated supply chain preferable in this narrow respect). For example,the 20% margin of error may be driven largely by the uncertain EFassociated with the electricity consumption (CO2e per kWh); still, ifthe actual electricity consumption by product A is significantly (e.g.,p<0.05) smaller than that by product B (and all else being equal), thenthe total CF of A is significantly smaller, even though the respectivemargins of error overlap. A straight forward comparison, however, ispossible only because (i) focusing on the electricity consumption and(ii) confirming that “all else is equal” are facilitated by a uniformdata structure.

Second, data amount and accuracy are balanced. A “one data structurefits all” framework enables CF for many products/services virtuallysimultaneously by looping the same, generic algorithm in the software.In one example, the data structure is such that it uniformly quantifiesGHG emissions for (road) transportation of a product from factory tomarket as a single “leg”, e.g., EF multiplied by transported massmultiplied by distance. This enables, for example, the use of estimated“distance to market” parameters that are used as default input data forall products in a certain region, so that a CF can be estimated even ifa product-specific distance has not yet been entered into the software.In essence, this enables using a single data entry across hundreds andthousands of appropriate products, thus reducing the volume of requireddata entry. Third, a uniform data structure provides meaningfulreporting and reduction analysis.

There is a direct relationship and hence synergies between the variousLCA stages that count towards a product/service CF and corporate carbonaccounting. These synergies are readily exploitable only if the LCAtaxonomy in the footprinting model has been set up accordingly and isapplied universally across all products/services. For example, theenergy consumed during the “production” phase of a product CF alsocontributes to scopes 1 and 2 of the corporate footprint. Using methodsand systems according to the disclosed subject matter, factory energyconsumption data has to be collected only once, and can then be usedboth for scope 1 of corporate carbon accounting and for the “production”portion of the product/service CF (followed by allocation to individualproducts). Similarly, the cradle-to gate portion of a full product CFcounts toward the scope 3 emissions of a customer's corporate footprint.Therefore, any company that has quantified its products' CFs can reportthe same results when approached by its corporate customers for scope3-relevant information. The company merely has to exclude thecontributions from LCA phases “distribution and retail”, “use phase”,and “disposal” to convert the CFs from cradle-to-grave to cradle-to-gate(and possibly adjust the outbound transportation phase from“plant-to-retailer” to “plant-to-customer”).

A uniform data structure enables integrated reporting across hundreds orthousands of products/services. For example, what is the GHGcontribution of making the purchased goods vs. transporting them to thecompany's plants? What is the GHG reduction potential—cumulative acrossall affected products—if these goods were sourced more locally ortransported by rail vs. road? Such analyses are easier to carry out ifthe CF of every product follows the same taxonomy of life cycle stagesand input data.

Although the invention has been described and illustrated with respectto exemplary embodiments thereof, it should be understood by thoseskilled in the art that the foregoing and various other changes,omissions and additions may be made therein and thereto, without partingfrom the spirit and scope of the present invention.

What is claimed is:
 1. A non-transitory computer storage media encodedwith computer program instructions that, when executed by one or moreprocessors, cause a computer device to perform operations fordetermining the carbon footprint of an item, said operations comprising:categorizing at least one predetermined resource in a cluster of similarresources; obtaining an average estimated emission factor (EF_(Cluster,i)) for each cluster; obtaining an estimated price (Price_(i))of each of said at least one predetermined resource; calculating saidemission factor (EF_(i,estimated)) according to the following:EF _(i,estimated) =a+b·ln( EF _(Cluster,i))+c·ln(Price_(i)) wherein a,b, and c are coefficients.
 2. A system for determining the carbonfootprint of an item, said system comprising: a data automation modulefor obtaining data related to predetermined resources associated with anitem from predetermined data sources; an emission factor estimatormodule including a non-transitory computer-readable medium havingcomputer executable instructions for calculating estimated emissionfactors for each of said resources, said instructions including:categorizing a resource in a predetermined cluster of similar resources;obtaining an average estimated emission factor (EF _(Cluster,i)) foreach cluster; obtaining an estimated price (Price_(i)) of said resource;calculating said emission factor (EF_(estimated,i)) according to thefollowing:EF _(i,estimated) =a+b·ln( EF _(Cluster,i))+c·ln(Price_(i)) wherein a,b, and c are coefficients. a calculation module for utilizing datarelated to said resources and said estimated emission factors for saidresources to determine a carbon footprint of said item.